Image noise reduction processing changes the granularity of image noise. Noise reduction processing is effective for the image reconstructed by iterative approximation processing. On the other hand, the granularity greatly deteriorates. Many observers have long experience in observing images having certain granularities.
The prior art includes a method of combining a “reconstructed image having undergone noise reduction by iterative approximation processing” with an “original image”. Combining the original image will add granularity components to the image having undergone noise reduction processing, thereby minimizing a sense of discomfort in appearance.
A problem of this technique is that the artifact components of the original image are also added to the reconstructed image to result in a reduction in image improving effect. When obtaining granularity like that of an image reconstructed by filtered back projection processing (to be referred to as an FBP image hereinafter) from a “reconstructed image having undergone noise reduction by iterative approximation processing”, if the input source data is minority data, a deterioration in image quality becomes noticeable because aliasing artifact is noticeable in FBP reconstruction for the reconstruction of an original image.
Another problem in the prior art is that although FBP reconstruction can control granularity by controlling the frequency characteristics of a ramp filter called a reconstruction function, it is theoretically difficult to provide a unit for operating granularity for an image reconstructed by iterative approximation processing. This makes it difficult to obtain granularity in accordance with the preferences of customers.